The model discussed here is offered as a prototype of the use of a computational model to explore alternate hypotheses and to suggest possible answers to some of the questions which have been addressed in the study of language acquisition. Why does not the child end up with an overly generalized grammar or lexicon? There is much evidence concerning the kinds of generalizations and over-generalizations that children make. However if we permit no overt and specific correction of the child's errors, then how is it that errors of over-generalization do not persist into adult speech? One answer to this question is proffered by attaching a system of weights to hypotheses. There are two related problems to be solved. Some mechanism in the model must allow erroneous hypotheses to be corrected; in addition there must be a way that more mature constructs can replace earlier ones. The model accomplishes these two tasks by means of a system of weights which represent confidence values and recency values. By this system more frequently matched constructs are preferred over less frequently matched constructs, and more recent hypotheses are favored for testing. This learning paradigm is illustrated by a set of procedures for learning the past tense of verbs in English. The scheme has the advantage that for a period of time when confidence factors are approximately in balance two or more constructs can co-exist. Thus we need not talk of rules or individual cases which have been learned or have not yet been learned but rather of a continuum in which rule schemas are either strong or weak.